1. Field of Art
The present invention generally relates to the field of digital video, and more specifically, to methods of correlating demographic data with characteristics of video content.
2. Background of the Invention
Video hosting sites, such as YouTube, currently have millions of users and tens of millions of videos. Users may sometimes have difficulty in determining which videos would be of interest to them and may be daunted by the sheer volume of videos available for viewing. Thus, the ability to suggest which videos would be of interest to a given user in view of the user's demographic attributes would be highly valuable. Similarly, the ability to infer the demographic attributes of a user based on past videos viewed by the user would be beneficial for a number of applications, such as providing user-specific content based on user demographics for users who have not already provided such data in a user profile.
However, conventional systems typically merely rely on external metadata associated with videos, such as keywords or textual video descriptions, to predict demographic groups that would be interested in a particular video, or to estimate the demographic attributes of a particular user. For example, conventional systems might recommend videos having keywords matching those specified in a viewer profile as being of interest to that viewer. However, if the video is new and has not yet been viewed and rated, and if the associated title is “spam” that misrepresents the true content of the video, then the conventional approach produces spurious suggestions. Thus, one shortcoming of conventional approaches is that they rely on external metadata that may be false when assessing the pertinence of a given video to a particular viewer, rather than examining actual data related to past viewings of video, such as content of the videos viewed, or information on user viewing sessions themselves.